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Master’s in Fraud, Anomalies and Advanced Detection in Mobility Insurance Engineering

Online master's programme of 1,900 hours in Fraud, Anomaly and Advanced Detection Engineering in Mobility Insurance. 12 modules covering fraud typologies, detection data, anomaly statistics, supervised and unsupervised learning, network and graph analysis, telematics, text and image forensics, SIU investigation and the legal framework, with an assigned technical tutor.

Duration1900 h
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Identify the main fraud typologies in mobility insurance.

02

Integrate data sources for anomaly detection.

03

Develop supervised and unsupervised fraud detection models.

04

Apply graph analysis to dismantle organised fraud networks.

05

Use telematics data and images as technical evidence.

06

Organise investigation units in line with the legal framework.

07

Measure the economic impact of an anti-fraud strategy.

Profile

Who it's for

  • Anti-fraud analysts who want to move from rules to advanced detection models.
  • SIU claims investigators looking to work with data, graphs and digital evidence.
  • Data scientists building anomaly and fraud detection systems for insurers.
  • Heads of investigation units responsible for strategy and results.
  • Motor loss adjusters who want to strengthen their technical fraud analysis.
  • Fraud prevention consultants advising insurers and mobility operators.
  • Engineering or science graduates aiming to enter motor insurance.
Standards & tools

What you'll work with

  • Regulation (EU) 2016/679 (GDPR: personal data protection)
  • Organic Law 3/2018 (Spanish data protection and digital rights law)
  • Organic Law 10/1995 (Spanish Criminal Code, including insurance fraud)
  • Regulation (EU) 2024/1689 (EU Artificial Intelligence Act)
  • Law 20/2015 (LOSSEAR: regulation, supervision and solvency of insurers)
  • Law 5/2014 (Spanish Private Security Act)
  • ISO/IEC 27037:2012 (identification, collection and preservation of digital evidence)
  • Neo4j
  • Python (scikit-learn, PyOD)
  • SAS Fraud Management
  • Shift Technology
  • i2 Analyst's Notebook
Syllabus

12 modules · 1900 h

  1. 1.1Typology of motor insurance fraud
  2. 1.2Opportunistic versus organised fraud
  3. 1.3Staged and induced accidents
  4. 1.4Inflation of damage and injuries
  5. 1.5Fraud at policy inception
  6. 1.6Internal and supplier fraud
  7. 1.7Fraud in shared mobility
  8. 1.8Economic impact of fraud
  9. 1.9Classic red-flag indicators
  10. 1.10Case study: analysis of an organised fraud file
  1. 2.1Policy, claims and payment data
  2. 2.2Accident telematics data
  3. 2.3Shared industry databases
  4. 2.4Open sources and OSINT
  5. 2.5Repair shop and loss adjuster data
  6. 2.6Injury and medical centre data
  7. 2.7Data quality and entity de-duplication
  8. 2.8Entity resolution
  9. 2.9Integration of unstructured data
  10. 2.10Practical exercise: entity resolution in a claims file
  1. 3.1Definition of anomaly and outlier
  2. 3.2Univariate and robust methods
  3. 3.3Mahalanobis distance
  4. 3.4Benford's law
  5. 3.5Time-series analysis of claims
  6. 3.6Detection of structural breaks
  7. 3.7Density models
  8. 3.8Statistical tests for patterns
  9. 3.9False positive rate
  10. 3.10Lab: applying Benford's law to repair amounts
  1. 4.1Case labelling and investigation bias
  2. 4.2Imbalanced classes and resampling
  3. 4.3Baseline logistic regression
  4. 4.4Gradient boosting for fraud
  5. 4.5Metrics: precision, recall and PR curve
  6. 4.6Cost-based threshold tuning
  7. 4.7Explainability for investigators
  8. 4.8Feedback of investigation outcomes
  9. 4.9Drift of adaptive fraud
  10. 4.10Practical exercise: suspicion scoring model
  1. 5.1Isolation Forest
  2. 5.2Local Outlier Factor
  3. 5.3Autoencoders for anomalies
  4. 5.4Claims clustering
  5. 5.5One-class SVM
  6. 5.6Combining detectors
  7. 5.7Evaluation without labels
  8. 5.8Semi-supervised learning
  9. 5.9Active learning with investigators
  10. 5.10Lab: autoencoder on property damage claims
  1. 6.1Graph theory applied to fraud
  2. 6.2Building the claims graph
  3. 6.3Centrality measures
  4. 6.4Community detection
  5. 6.5Fraud ring patterns
  6. 6.6Graph databases
  7. 6.7Graph neural networks
  8. 6.8Network visualisation
  9. 6.9Temporal network analysis
  10. 6.10Case study: identifying a fraud network with Neo4j
  1. 7.1Verification of accident time and location
  2. 7.2Impact profile with accelerometers
  3. 7.3Consistency between damage and impact force
  4. 7.4Detection of staged accidents
  5. 7.5Tampering with telematics devices
  6. 7.6Mileage fraud in usage-based products
  7. 7.7Connected vehicle data
  8. 7.8Chain of custody of telematics data
  9. 7.9Evidentiary value in court
  10. 7.10Practical exercise: verifying a claim with impact data
  1. 8.1Natural language processing of statements
  2. 8.2Entity extraction from reports
  3. 8.3Detection of duplicate texts
  4. 8.4Language models to summarise case files
  5. 8.5Forensic analysis of photographs
  6. 8.6EXIF metadata and image manipulation
  7. 8.7Detection of AI-generated images
  8. 8.8Reuse of photos across claims
  9. 8.9Forged documents
  10. 8.10Lab: detection of reused images
  1. 9.1Organisation of an investigation unit
  2. 9.2Alert prioritisation
  3. 9.3Investigation procedures
  4. 9.4Interviews and statements
  5. 9.5Working with private investigators
  6. 9.6Digital evidence and its preservation
  7. 9.7Coordination with law enforcement
  8. 9.8Criminal complaints and court proceedings
  9. 9.9Recoveries and civil actions
  10. 9.10Case study: investigation plan for a suspicious claim
  1. 10.1Insurance fraud in the Spanish Criminal Code
  2. 10.2GDPR and processing for anti-fraud purposes
  3. 10.3Common insurance industry databases
  4. 10.4Automated decisions and human oversight
  5. 10.5The AI Act and assessment systems
  6. 10.6Rights of the policyholder
  7. 10.7Private security and Law 5/2014
  8. 10.8Anti-money laundering
  9. 10.9Bias and discrimination in detection
  10. 10.10Workshop: impact assessment of an anti-fraud system
  1. 11.1Corporate anti-fraud strategy
  2. 11.2Calculating return on investment
  3. 11.3Indicators: detection rate and savings
  4. 11.4Balancing fraud control and customer experience
  5. 11.5Deterrence and prevention at policy inception
  6. 11.6Industry collaboration
  7. 11.7Management of technology providers
  8. 11.8Implementation in claims systems
  9. 11.9Continuous improvement
  10. 11.10Practical exercise: anti-fraud dashboard
  1. 12.1Definition of the type of fraud to address
  2. 12.2State-of-the-art review
  3. 12.3Data preparation and anonymisation
  4. 12.4Design of the detection architecture
  5. 12.5Model development
  6. 12.6Evaluation with business metrics
  7. 12.7Integration into the investigation process
  8. 12.8Legal and ethical analysis
  9. 12.9Master's Thesis report
  10. 12.10Master's Thesis: defence before a panel
Methodology, software & labs

How you'll learn

100% online training on the SEIUM campus, with downloadable materials, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within each intake, so the programme is compatible with work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is Neo4j, Python (scikit-learn, PyOD), SAS Fraud Management, Shift Technology and i2 Analyst's Notebook, in free, academic or evaluation versions. Exercises are based on real procedures: detection of organised fraud networks using graphs, claims verification with telematics impact data, and forensic analysis of images submitted with claims. Assessment is continuous by module, plus the final Master's Thesis.

Standards covered: Regulation (EU) 2016/679 (GDPR), Organic Law 3/2018 on Data Protection and Guarantee of Digital Rights, Organic Law 10/1995 of the Criminal Code, Regulation (EU) 2024/1689 on Artificial Intelligence, Law 20/2015 (LOSSEAR), Law 5/2014 on Private Security and ISO/IEC 27037:2012.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of the GDPR, the EU AI Act, the Criminal Code and ISO/IEC 27037 applied to real cases, not as a theoretical annex.
Industry software:Neo4j, Python (scikit-learn, PyOD), SAS Fraud Management, Shift Technology and i2 Analyst's Notebook, in free, academic or evaluation versions.
Master's Thesis with defence:a complete fraud detection architecture, evaluated with business metrics and integrated into the investigation process, defended before a panel.
Flexible format:100% online, no fixed timetables, compatible with full-time work.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Entity resolution
Resolve and de-duplicate entities in a claims file to prepare detection data.
Suspicion scoring
Build and tune a supervised suspicion scoring model with cost-based thresholds.
Fraud network detection
Identify an organised fraud ring by graph analysis with Neo4j.
Telematics and image evidence
Verify a claim with impact data and detect reused images.
Master's Thesis
Design, develop and evaluate a fraud detection system with its legal and ethical analysis, defended before a panel.
Career opportunities

Where this takes you

Anti-fraud analystSIU claims investigatorAnti-fraud data scientistHead of investigation unitMotor loss adjusterFraud prevention consultant
Admissions & scholarships

Requirements

  • University degree in engineering, science or architecture, or equivalent demonstrable professional experience.
  • Internet connection and a computer capable of running the programme software.
  • English sufficient to read technical standards.

Ready to start?

Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.

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FAQ

Frequently asked questions

The programme totals 1,900 hours over 12–14 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.

You work with Neo4j, Python (scikit-learn, PyOD), SAS Fraud Management, Shift Technology and i2 Analyst's Notebook, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.

A SEIUM Institute diploma, awarded on passing the continuous assessment and the Master's Thesis. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.

Yes. Modules passed in a course or postgraduate diploma in the same area are recognised in the higher programme. Ask about the recognition policy when you enrol.

A university degree in engineering, science or architecture, or equivalent professional experience, plus a suitable computer and English sufficient to read technical standards.

Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.

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